.. _coding_guidelines: ================= Coding Guidelines ================= Introduction ------------ We write code for OpenStack charms. Mostly in Python. They say that code is read roughly 20 times more than writing it, and that’s just the process of writing code. Reviewing code and modifying it means that it will be read many, many times. Let’s make it as easy as possible. We’re lucky(!) with Python as the syntax ensures that it roughly always looks the same. As OpenStack charms are for OpenStack it’s a good idea to adhere to the OpenStack Python coding standard. So first things first: * Read the `OpenStack Coding standard `__. * Read PEP8 (again). Topics ------ Multiple roots -- symlinks ~~~~~~~~~~~~~~~~~~~~~~~~~~ Multiple roots with symlinks create issues in charms. This is where, for example, charmhelpers is symlinked into both a hooks and actions subdirectory. This creates a situation where the *same* modules are loaded into the Python interpreter's memory twice at two different module paths. This creates problems with testing as *depending on the load time ordering* it might not be clear which particular module path you're trying to mock out and which one is first in the module path map. So only every have ONE root for your python code in a charm. e.g. put it in ``/lib`` and add that to path by ``sys.path.append(‘lib’).`` Incidentally, if you **are** mocking out code in charmhelpers in you charms, **it's probably not a good idea**. Only mock code in the target object file, rather than an included module. Install-time vs Load-time vs Runtime code ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The hooks in charms are effectively short-term running scripts. However, despite being short-lived, the code invoked is often complex with multiple modules being imported which also import other modules. It’s important to be clear on what is *load time* code and _runtime_ code. Although there is no actual distinction in Python, it’s useful to think of *runtime* starting when the following code is reached: .. code:: python if __name__ == ‘__main__’: do_something() I.e. the code execution of ``do_something()`` is runtime, with everything preceding being loadtime. So why is the distinction useful? Put simply, *it’s much harder to test* load-time code in comparison to runtime code with respect to mocking. Consider these two fragments: **Bad:** .. code:: python import a.something OUR_CONFIG = { ‘some_thing’: a.something.config(‘a-value’), } **Good:** .. code:: python import a.something def get_our_config(): return { ‘some_thing’: a.something.config(‘a-value’), } If performance is an issue (i.e. multiple calls to ``config()`` are expensive) then either use a ``@caching`` type decorator, or just doing it manually. e.g. .. code:: python _our_config = None def get_our_config(): if _our_config is None: _our_config = { 'some_thing': a.something.config('a-value'), } return _our_config In the bad example, in order to mock out the config module we have to do something like: .. code:: python with patch(‘a.something.config’) as mock_config: import a.something.config This also relies on this being the _first_ time that module has been imported. Otherwise, the module is already cached and config can’t be mocked out. Compare this with the good example. .. code:: python def test_out_config(self): with patch(‘module.a.something.config’) as mock_config: mock_config.return_value = ‘thing’ x = model.get_out_config() This brings us to: CONSTANTS should be simple ~~~~~~~~~~~~~~~~~~~~~~~~~~ In the bad example above, the constant ``OUR_CONFIG`` is defined as load-time by calling ``a.something.config()``. Thus, in reality, the constant is being defined at load-time using a runtime function that returns a value - it's dynamic. Don’t: .. code:: python CONFIG = { ‘some_key’: config(‘something’), } This is actually a *function in disguise*. Prefer: .. code:: python def get_config(): return { ‘some_key’: config(‘something’), } Why? So that you can mock out ``get_config()`` or ``config()`` at the test run time, rather than before the module loads. This makes testing easier, more predictable, and also makes it obvious that it’s not really a constant, but actually a function which returns a structure that is dynamically generated from configuration. And **definitely** don’t do this at the top level in a file: .. code:: python CONFIGS = register_configs() You’ve just created a load time test problem _and_ created a CONSTANT that isn’t really one. Just use ``register_configs()`` directly in the code and write ``register_configs()`` to be ``@cached`` if performance is an issue. Decorators ~~~~~~~~~~ There shouldn't be much need to write a decorator. They definitely **should not** be used instead of function application or instead of context managers. When they are used it's preferable that they are orthogonal to the function they are decorating, and don't change the nature of the function. functools.wraps(f) ++++++++++++++++++ If they are used, then they should definitely make use of ``functools.wraps`` to preserve the function name of the original function and it's docstring. This makes stacktraces more readable. e.g.: .. code:: python def my_decorator(f): functools.wraps(f): def decoration(*args, **kwargs): # do soemthing before the function call? r = f(*args, **kwargs) # do soemthing after the function call? return r return decoration Mocking out decorators ++++++++++++++++++++++ If the decorator's functionality is orthogonal to the function, then mocking out the decorator shouldn't be necessary. However, if it *isn't* then tweaking how the decorator is written can make it easier to mock out the decorator. Consider the following code: .. code:: python @a_decorator("Hello") def some_function(): pass def a_decorator(name): def outer(f): @functools.wraps(f) def inner(*args, **kwargs): # do something before the function r = f(*args, **kwargs) # do something after the function return r return inner return outer It's very difficult to test some_function without invoking the decorator, and equally, it's difficult to stop the decorator from being applied to the function without mocking out ``@a_decorator`` before importing the module under test. However, with a little tweaking of the decorator we can mock out the decorator without having to jump through hoops: .. code:: python def a_decorator(name): def outer(f): @functools.wraps(f) def inner(*args, **kwargs): return _inner(name, args, kwargs) return inner return outer def _inner(name, args, kwargs): # do something before the function r = f(*args, **kwargs) # do something afterwards return r Now, we can easily mock ``_inner()`` after the module has been loaded, thus changing the function of the decorator _after_ it has been applied. Import ordering and style ~~~~~~~~~~~~~~~~~~~~~~~~~ Let's be consistent and ensure that we have the same import ordering and style across all of the charms (and other code) that we release. Use absolute imports ++++++++++++++++++++ Use absolute imports. In Python 2 code this means also that we should force absolute imports: .. code:: python from __future__ import absolute_import We should use absolute imports so that we don't run into module name clashes across our own modules, nor with system and 3rd party packages. See https://www.python.org/dev/peps/pep-0328/#id8 for more details. Import ordering +++++++++++++++ * Core Python system packages * Third party modules * Local modules They should be be alphabetical order, with a single space between them, and preferably in alphabetical order. If load order is important (and it shouldn’t be!) then that’s the only reason they shouldn’t be in alpha order. Import Style ++++++++++++ It's preferable to import a module rather than an object, class, function or instance from a module. Prefer: .. code:: python import module module.function() over: .. code:: python from module import function function() However, if there are good reasons to import from a module, and there is more than one item, then the style is: .. code:: python from module import ( one_import_per_line, ) Why? Using ``import module; module.function()`` rather than ``from module import function`` is preferable because: * with multiple imports, more symbols are being brought into the importing modules namespace. * It's clearer in the code when an external function is being used, as it is always prefixed by the external module name. This is useful as it makes it more obvious what is happening in the code. Only patch mocks in the file/module under test ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A unit test often needs to mock out functions, classes or instances in the file under test. The mocks should _only_ be applied to the file that contains the item that is being tested. Don't: .. code:: python # object.py import something def function_under_test(x): return something.doing(x) In the unit test file: ``test_unit.py``: .. code:: python # test_unit.py def unit_test(): with patch('something.doing') as y: y.return_value = 5 assert function_under_test(3) == 5 Prefer: .. code:: python # object.py import something def function_under_test(x): return something.doing(x) In the unit test file: ``test_unit.py``: .. code:: python # test_unit.py def unit_test(): with patch('object.something.doing') as y: y.return_value = 5 assert function_under_test(3) == 5 i.e. the thing that is patched is in object.py **not** in the library file 'something.py' Don't use _underscore_methods outside of the class ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Underscore methods are supposed to be, by convention, private to the enclosing scope, be that a module or a class. They are used to signal that the method is _private_ even though the privacy can't be enforced. Thus don't do this: .. code:: python class A(): def _private_method(): pass x = A() x._private_method() Simply rename the method without the underscore. Otherwise you break the convention and people will not understand how you are using *private methods*. Equally, don't use them in derived classes _either_. A private method is supposed to be private to the class, and not used in derived classes. Only use list comprehensions when you want the list ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Don’t: .. code:: python [do_something_with(thing) for thing in mylist] Prefer: .. code:: python for thing in mylist: do_something_with(thing) Why? You just created a list and then threw it away. And it’s actually less clear what you are doing. Do use list comprehensions when you actually want a list to do something with. Avoid C-style dictionary access in loops ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Don’t: .. code:: python for key in dictionary: do_something_with(key, dictionary[key]) Prefer: .. code:: python for key, value in dictionary.items(): do_something_with(key, value) Why? Using a list of keys to access a dictionary is less efficient and less obvious as to what’s happening. ``key, value`` could actually be ``config_name`` and ``config_item`` which means the code is more self-documenting. Also remember that ``dictionary.keys()`` & ``dictionary.values()`` exist if you want to explicitly iterate just over the keys or values of a dictionary. Also, it’s preferable to iterate of ``dictionary.keys()`` rather than ``dictionary`` because, whilst they do the same thing, it’s not as obvious what is happening. If performance is an issue (Python2) then ``iterkeys()`` and ``itervalues()`` for generators, which is the default on Python3. Prefer tuples to lists ~~~~~~~~~~~~~~~~~~~~~~ Tuples are non malleable lists, and should be used where the list isn’t going to change. They have (slight) performance advantages, but come with a guarantee that the list won’t change - note the objects within the tuple could change, just not their position or reference. Thus don’t: .. code:: python if x in [‘hello’, ‘there’]: do_something() Prefer: .. code:: python if x in (‘hello’, ‘there’): do_something() However, remember the caveat. A single item tuple literal has to have a trailing comma: .. code:: python my_tuple = (‘item’, ) Prefer CONSTANTS to string literals or numbers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is the “No magic numbers” rule. In a lot of the OS charms there is code like: .. code:: python db = kv() previous_thing = db.get('thing_key', thing) Prefer: .. code:: python THING_KEY = ‘thing_key’ db = kv() previous_thing = db.get(THING_KEY, thing) Why? String literals introduce a vector for mistakes. We can’t use the language to help prevent spelling mistakes, nor our tools to do autocompletion, nor use lint to find ‘undefined’ variables. This also means that if you use the same number or string literal more than once in code you should create a constant for that value and use that in code. This includes fixed array accesses, offsets, etc. Don’t abuse __call__() ~~~~~~~~~~~~~~~~~~~~~~ ``__call__()`` is a method that is invoked when ``()`` is invoked on an object -- ``()`` on a class invokes ``__call__`` on the metaclass for the class. A good example of abuse of ``__call__`` is the class ``HookData()`` which, to access the context manager, is invoked as: .. code:: python with HookData()() as hd: hd.kv.set(...) The sequence ``()()`` is almost certainly a *code smell*. There is hidden behaviour that requires you to go to the class to see what is actually happening. It would have been more obvious if that method was just called ``cm()`` or ``context()``: .. code:: python with HookData().context() as hd: hd.kv.set(...) Don’t use old style string interpolation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: python action_fail("Cannot remove service: %s" % service.host) Prefer: .. code:: python action_fail("Cannot remove service: {}".format(service.host)) Why? It’s the new style, and the old style is deprecated; eventually it will be removed. Plus the new style is way more powerful: keywords, dictionary support, to name but a few. Docstrings and comments ~~~~~~~~~~~~~~~~~~~~~~~ Docstrings and comments are there to inform a reader of the code additional, contextual, information that isn't readily available by just reading the code. Docstrings can also be used to automatically generate *useful* documentation for programmers who are using those functions. This is particularly important in the case of a library, but is also very important simply from a maintenance perspective. Being able to look at the docstring for a function and quickly understand the types of the parameters and the return type helps to understand the code *much more quickly* than hunting through other code trying to understand what types of things might be sent to the function. In futher, types in docstrings will become part of the *linting* of the code (as part of PEP8) and so, good practice now, will help with more maintainable code in the future. Comments are important to help the reader of the code understand what is being implemented, rather than just repeating what the code does. A good comment is minimal and terse, yet still explains the purpose behind a segment of code. Docstring formats are slightly complicated by whether we are doing Python 2 code, Python 3 code, or a shared library. For Python 2 and Python 2 AND 3 compatible code (e.g. charm-helpers) there is a preferred approach, and for Python 3 only code there is a separate preferred approach. Python 2 code and Python 2/3 compatible code -------------------------------------------- Python 2 compatible code docstrings are constrained by not being able to have mypy_ annotations in the code. We don't really want to add mypy annotations into comments, so we've adopted a docstring convention which informs as to what the types are, without being able to actually statically check it. The main reason for *not* using mypy compatible comments is that they are fairly ugly. As we are not using, nor plan to use, mypy_ on Python 2 code, we can do something that is a little more aesthetically pleasing. Every function exported by a module should have a docstring. Generally, this means all functions mentioned in ``__ALL__`` or implicitly those that do not start with an ``_``. The preferred format for documenting parameters and return values is ReStructuredText (reST) as described: http://docutils.sourceforge.net/rst.html but with mypy type signatures. Classes will use the ``:class:`ClassName``` type declaration so that sphinx can appropriately underline when using autodoc. The field lists are described here: http://www.sphinx-doc.org/en/stable/domains.html#info-field-lists An example of an acceptable function docstring is: .. code:: python def mult(a, b): """Multiple a * b and return the result. :param a: Number :type: Union[int, float] :param b: Number :type: Union[int, float] :returns a * b :rtype: Union[int, float] :raises: ValueError, TypeError if the params are not numbers """ return a * b def some_function(a): """Do something with the FineObject a :param a: a fine object :type: :class:`FineObject` """ do_something_with(a) Other comments should be used to support the code, but not just re-say what the code is doing. Python 3 code ------------- The situation is a little more complicated for Python 3 code. Ideally, we would just use Python 3.6 mypy_ annotations, but Xenial *only* has Python 3.5. This means that some types of annotations aren't possible. As Xenial is supported until 2021, until that time, all Python 3 mypy_ annotations will need to be supported on Python 3.5. This means that PEP-526 can't be used (Syntax for variable annotations) and PEP-525 (Asynchronous generators) and PEP-530 (comprehensions) are also not possible. So the minimal preferred docstring format for Python 3 code is the same as Python 2. However, ideally, mypy_ notations will be used: .. code:: python def mult(a: Union[int, float], b: Union[int, float]) -> Union[int, float]: """Multiple a * b and return the result""" return a * b def some_function(a: FineObject): """Do something with a FineObject :param: a is used in the context of doing something. """ do_something_with(a) .. note:: Because mypy annotations tell you what the types are and this type information can be checked statically, it means that we don't have to specify what the function might raise as an exception, as that would be a type error. e.g. if at runtime the function ``mult(...)`` was supplied with an object that had no ``*`` implementation, then the code would raise an exception. However, linting on fully typed code would prevent this. Hence we don't, for function ``mult`` need to provide either a return type in the docstring, nor a ``:raises:`` line. In the ``some_function(...)`` we have optionally specified the ``:param:`` to provide additional information to the docstring for the user. The type will be provided by ``sphinx`` autodoc. The end objective with the Python 3 code is to use mypy_ (or pyre_) to statically check the code in the CI server prior to check-ins. .. _mypy: http://mypy-lang.org/ .. _pyre: https://pyre-check.org/ Ensure there's a comma on the last item of a dictionary ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This helps when the developer adds an item to a dictionary literal, in that they don't have to edit the previous line to add a comma. It also means that the review doesn't indicate that the previous line has changed (due to the addition of a comma). Prefer: .. code:: python a_dict = { 'one': 1, 'two': 2, } over: .. code:: python a_dict = { 'one': 1, 'two': 2 } Avoid dynamic default arguments in functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Don't use a dynamic assignment to a default argument. e.g. .. code:: python def a(b=[]): b.append('hello') print b In [2]: a() ['hello'] In [3]: a() ['hello', 'hello'] As you can see, the list is only assigned the first time, and thereafter it 'remember' the previous values. Also avoid other default, dynamic, assignments: .. code:: python def f(): return ['Hello'] def a(b=f()): b.append('there') print b In [3]: a() ['Hello', 'there'] In [4]: a() ['Hello', 'there', 'there'] Instead, prefer: .. code:: python def a(b=None): if b is None: b = f() b.append('there') print b In [6]: a() ['Hello', 'there'] In [7]: a() ['Hello', 'there'] Why? Although it can be a handy side-effect for allowing a function to remember previous values, due to a quirk in the interpreter in only assigning the reference once, it may be changed in the future and it hides the intention of the code. Avoid side effects in Adapters and Contexts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Adapters (reactive charms) and Contexts should not alter the unit they are running, i.e. should not have unexpected side effects. Some environment altering side effects do exist in older contexts, however this should not be taken as an indicator that it is acceptable to add more. Why? Adapters and Contexts are regulary called via the update status hook to assess whether a charm is ready. If calling the Context or Adapter has unexpected side effects it could interrupt service. See `Bug #1605184 `__ for an example of this issue.